Traditional data science project deployments typically involve complex, time-consuming processes that take data scientists away from what they do best: exploring and analyzing data. But why place this burden on your data science team when Anaconda Enterprise can handle the process instead–all with the single click of a button?

Big Data, advanced analytics and scientific computing bring exciting opportunities for businesses to leverage more and richer types of data to create meaningful business value and impact. However, it also creates serious computational challenges. To effectively manage this increasingly complex landscape, Data Science teams need technologies that will easily scale and take advantage of the available processing power from their desktop to high performance clusters with the latest advances in chipsets.

The Anaconda platform easily delivers high performance analysis to Data Science teams by leveraging investments in all types of infrastructure to both Scale Up and Scale Out workloads.

In this paper, you’ll discover why Anaconda, the leading Open Data Science platform, is the right solution to deliver that range of flexibility and performance.

Open Data Science is an inclusive movement that makes the open source tools of data science - data, analytics and computation — easily work together as a connected ecosystem. However, not all open source data science tools and platforms fully embrace the open connectedness of this movement.

Anaconda, a modern open source analytics platform powered by Python, is the leading full-stack Open Data Science platform, addressing difficult and complex operational problems while providing Data Science teams with the power of the latest innovations in open source analytics. Anaconda also makes it easy to collaborate across the entire Data Science team, no matter where in the world members may be located.